Patients as teachers: a within-subjects randomized pilot experiment of patient-led online learning modules for health professionals
Bibliographic record
Abstract
Abstract Purpose Many health professions education programs involve people with lived experience as expert speakers. Such presentations may help learners better understand the realities of living with chronic illness or experiencing an acute health problem. However, lectures from only one or a small number of people may not adequately illustrate the perspectives and experiences of a diverse patient cohort. Additionally, logistical constraints such as public health restrictions or travel barriers may impede in-person presentations, particularly among people who have more restrictions on their time. Health professions education programs may benefit from understanding the potential effects of online patient-led presentations with a diverse set of speakers. We aimed to explore whether patient-led online learning modules about diabetes care would influence learners’ responses to clinical scenarios and to collect learners’ feedback about the modules. Method This within-subjects randomized experiment involved 26 third-year medical students at Université Laval in Quebec, Canada. Participation in the experiment was an optional component within a required course. Prior to the intervention, participating learners responded to three clinical scenarios randomly selected from a set of six such scenarios. Each participant responded to the other three scenarios after the intervention. The intervention consisted of patient-led online learning modules incorporating segments of narratives from 21 patient partners (11 racialized or Indigenous) describing why and how clinicians could provide patient-centered care. Working with clinical teachers and psychometric experts, we developed a scoring grid based on the biopsychosocial model and set 0.6 as a passing score. Independent evaluators, blinded to whether each response was collected before or after the intervention, then scored learners’ responses to scenarios using the grid. We used Fisher’s Exact test to compare proportions of passing scores before and after the intervention. Results Learners’ overall percentage of passing scores prior to the intervention was 66%. Following the intervention, the percentage of passing scores was 76% (p = 0.002). Overall, learners expressed appreciation and other positive feedback regarding the patient-led online learning modules. Discussion Findings from this experiment suggest that learners can learn to provide better patient-centered care by watching patient-led online learning modules created in collaboration with a diversity of patient partners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".